FedGuide: Diffusion Prior Alignment and Value Baseline Guidance for Heterogeneous Federated Reinforcement Learning
Read the original on arXiv Machine Learning →FedGuide is a federated reinforcement learning framework that tackles distributional mismatch among heterogeneous clients by using diffusion priors as personalized behavior models. It aggregates these priors via an Optimal-Transport Mixture-of-Experts, preserving diverse behavior modes, and introduces a Distribution Correction Estimation (DICE) value baseline to give low‑variance, return‑aware guidance for local policy improvement. Experiments on heterogeneous environments demonstrate that FedGuide surpasses existing FRL methods in client‑average returns, final‑round performance, and worst‑round robustness while maintaining stable learning under stronger heterogeneity.
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